Faster or fewer iterations? A strategic perspective of a sequential product development project

Abstract

Shortening the lead time for Product Development (PD) provides enterprises with a competitive advantage. Given the iterative nature of PD projects, two aspects are regularly considered to shorten the PD lead time, that is, conducting faster or fewer iterations. However, executing faster iterations usually causes more iterations and vice versa. Therefore, suitable coordination between faster and fewer iterations is necessary to minimize the PD lead time. We investigate this coordination from a strategic perspective, whereby a PD project is considered as a sequence of stages and characterized by the design rates and rework probabilities of those stages. We model the coordination as a decision to choose the appropriate design rates for each stage, wherein the rework probabilities are negatively related to the design rates. An absorbing Markov process is applied to calculate the expected lead time of a PD project. Further, we formulate a geometric programming model to determine the optimal design rates of the stages with respect to the minimal expected lead time. Several insights are extracted from the model to provide general guidance on the coordination, including the effect of the acceptance check rate of the project, rework risk of the stages on the optimal design rates, and decomposability of the coordination. Inspired by these insights, an efficient heuristic algorithm is designed. The algorithm performs well in numerical experiments, which in turn validates the insights. Additionally, a field case proves the effectiveness of our model. Compared with the current policy, 12.25% of the PD lead time is saved through appropriate coordination between faster and fewer iterations.

Keywords:

• Product development
• iteration
• lead time
• Markov process

Additional information

Notes on contributors

Maoqi Liu

Maoqi Liu is a PhD candidate in industrial engineering at Tsinghua University, China. His research interests include product design and development management, stochastic process and distributionally robust optimization. He earned his BS degree in mechanical design manufacture and automation from the School of Mechanical Engineering at Shandong University.

Li Zheng

Li Zheng is a professor in Department of Industrial Engineering at Tsinghua University, China. His current research interests are production system, manufacturing information system and digital manufacture. He has published numerous articles in many journals, such as European Journal of Operational Research, Computers & Operations Research, International Journal of Production Research, International Journal of Production Economics, etc.

Changchun Liu

Changchun Liu is a research fellow in IORA at National University of Singapore. His research interests are revenue management, supply chain management and optimization. He earned his PhD degree in the Department of Industrial Engineering at Tsinghua University, China. He has published his researches in many journals, such as European Journal of Operational Research, Annals of Operations Research, Transportation Research Part E, Computers & Industrial Engineering, International Journal of Production Research, etc.